A Systematic Review of AI-Enhanced Learning in Science Classrooms

ผู้แต่ง

  • Chanoknad Lomkate Faculty of Education, Mahasarakham University, Thailand
  • Chukiart Phuaphuang Faculty of Education, Mahasarakham University, Thailand
  • Thanakit Sangjam Faculty of Education, Mahasarakham University, Thailand
  • Patcharapon Chaimung Faculty of Education, Mahasarakham University, Thailand
  • Prasart Nuangchalerm Faculty of Education, Mahasarakham University, Thailand

คำสำคัญ:

Artificial Intelligence, Chatbot, Digital Technology, Science Education, Technology Education

บทคัดย่อ

Contemporary science learning increasingly involves artificial intelligence and its implications for the science classroom. This study utilizes the Education Resources Information Center (ERIC) database to explore AI-enhanced learning in science classrooms. The keyword “AI-enhanced learning science” was used for search along with the filters "artificial intelligence," “technology uses in education," and “science education” to screen relevant publications. A total of 23 articles were identified and screened. Eight articles were retained for critical review. The finding revealed that AI technology such as ChatGPT, AI chatbots, and AI robot image recognition are gaining significant attention in international research, particularly in promoting conceptual understanding and learner motivation. This review suggests that AI can be effectively integrated into science classrooms.

เอกสารอ้างอิง

Anik, I. A., Kamal, A. H. M., Kabir, M. A., Uddin, S., & Moni, M. A. (2024). A robust deep-learning model to detect major depressive disorder utilizing EEG signals. IEEE Transactions on Artificial Intelligence, 5(10), 4938–4947. https://ieeexplore.ieee.org/document/10510404

Ardyansyah, A., Yuwono, A. B., Rahayu, S., Alsulami, N. M., & Sulistina, O. (2024). Students' perspectives on the application of a generative pre-trained transformer (GPT) in chemistry learning: A case study in Indonesia. Journal of Chemical Education, 101(9), 3666–3675. https://doi.org/10.1021/acs.jchemed.4c00220

Chen, P. Y., & Liu, Y. C. (2024). Impact of AI robot image recognition technology on improving students’ conceptual understanding of cell division and science learning motivation. Journal of Baltic Science Education, 23(2), 208–220. https://doi.org/10.33225/jbse/24.23.208

Chiu, T. K. F., Xia, Q., Zhou, X., Chai, C. S., & Cheng, M. (2023). Systematic literature review on opportunities, challenges, and future research recommendations of artificial intelligence in education. Computers and Education: Artificial Intelligence, 4, Article 100118. https://doi.org/10.1016/j.caeai.2022.100118

Dimitriadou, E., & Lanitis, A. (2023). A critical evaluation, challenges, and future perspectives of using artificial intelligence and emerging technologies in smart classrooms. Smart Learning Environments, 10, Article 12. https://doi.org/10.1186/s40561-023-00231-3

Gachago, D., Huang, C. W., Immenga, C., Cox, G., Mosienyane, T., & Govender, S. (2024). Designing in the times of AI: Co-creation as a strategy towards emergent learning design. African Journal of Inter/Multidisciplinary Studies, 6(1), 1–13. https://doi.org/10.51415/ajims.v6i1.1536

Holmes, W. (2020). Artificial intelligence in education. In Encyclopedia of education and information technologies (pp. 88-103). Springer International Publishing.

Holmes, W., Porayska-Pomsta, K., Holstein, K., Sutherland, E., Baker, T., Shum, S. B., Santos, O. C., Rodrigo, M. T., Cukurova, M., Bittencourt, I. I., & Koedinger, K. R. (2022). Ethics of AI in education: Towards a community-wide framework. International Journal of Artificial Intelligence in Education, 32, 504–526. https://doi.org/10.1007/s40593-021-00239-1

Hooda, M., Rana, C., Dahiya, O., Rizwan, A., & Hossain, M. S. (2022). Artificial intelligence for assessment and feedback to enhance student success in higher education. Mathematical Problems in Engineering, 2022, Article 5215722. https://doi.org/10.1155/2022/5215722

Lee, J., An, T., Chu, H. E., Hong, H. G., & Martin, S. N. (2023). Improving science conceptual understanding and attitudes in elementary science classes through the development and application of a rule-based AI chatbot. Asia-Pacific Science Education, 9(2), 365–412. https://doi.org/10.1163/23641177-bja10070

Luckin, R. (2018). Machine learning and human intelligence: The future of education in the 21st century. UCL Institute of Education Press.

Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. Pearson.

Mnguni, L., Nuangchalerm, P., El Islami, R. A. Z., Sibanda, D., Sari, I. J., & Ramulumo, M. (2024). The behavioural intentions for integrating artificial intelligence in science teaching among pre-service science teachers in South Africa and Thailand. Computers and Education: Artificial Intelligence, 7, Article 100334. https://doi.org/10.1016/j.caeai.2024.100334

National Research Council. (2012). A framework for K–12 science education: Practices, crosscutting concepts, and core ideas. National Academies Press.

Nuangchalerm, P., & Saregar, A. (2024). Analysis of pedagogical applications and awareness issues of using chatbots in the science classroom. E3S Web of Conferences, 482, Article 05013. https://doi.org/10.1051/e3sconf/202448205013

Ng, D. T. K., Wu, W., Leung, J. K. L., Chiu, T. K. F., & Chu, S. K. W. (2024). Design and validation of the AI literacy questionnaire: The affective, behavioural, cognitive and ethical approach. British Journal of Educational Technology, 55, 1082–1104. https://doi.org/10.1111/bjet.13411

Slade, S., & Prinsloo, P. (2013). Learning analytics: Ethical issues and dilemmas. American Behavioral Scientist, 57(10), 1510–1529. https://doi.org/10.1177/00027642134793

Sung, S., Ding, X., Jiang, R., Sereiviene, E., Bulseco, D., & Xie, C. (2024). Using artificial intelligence teaching assistants to guide students in solar energy engineering design. Journal of Geoscience Education, 72(4), 347–366. https://doi.org/10.1080/10899995.2024.2384340

Williams, A. (2024). Comparison of generative AI performance on undergraduate and postgraduate written assessments in the biomedical sciences. International Journal of Educational Technology in Higher Education, 21, Article 52. https://doi.org/10.1186/s41239-024-00485-y

Wusylko, C., Antonenko, P., Abramowitz, B., Waisome, J., Perez, V., Killingsworth, S., & MacFadden, B. (2025). Supporting teachers in integrating machine learning into science instruction. Journal of Technology and Teacher Education, 33(1), 213–242. https://doi.org/10.70725/161207qresyx

Xu, W., & Ouyang, F. (2022). The application of AI technologies in STEM education: A systematic review from 2011 to 2021. International Journal of STEM Education, 9, Article 59. https://doi.org/10.1186/s40594-022-00377-5

Yip, D. Y. (2004). Questioning skills for conceptual change in science instruction. Journal of Biological Education, 38(2), 76–83. https://doi.org/10.1080/00219266.2004.9655905

Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education. International Journal of Educational Technology in Higher Education, 16, Article 39. https://doi.org/10.1186/s41239-019-0171-0

ดาวน์โหลด

เผยแพร่แล้ว

24-06-2026

รูปแบบการอ้างอิง

Lomkate, C., Phuaphuang, C., Sangjam, T., Chaimung, P., & Nuangchalerm, P. (2026). A Systematic Review of AI-Enhanced Learning in Science Classrooms. Research and Development Journal Suan Sunandha Rajabhat University, 18(1), 1–14. สืบค้น จาก https://so05.tci-thaijo.org/index.php/irdssru/article/view/284781

ฉบับ

ประเภทบทความ

บทความวิจัย